Papers with Code 아카이브
3D geometry
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yinyunie/RfDNet
공식 코드RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction
https://github.com/yorklyb/LiDAR-SFM
공식 코드L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration
https://github.com/yyyykf/fmqm
공식 코드Textured mesh Quality Assessment using Geometry and Color Field Similarity
https://github.com/za-cheng/PM-PMVS
공식 코드Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance
https://github.com/zaixizhang/molcode
공식 코드An Equivariant Generative Framework for Molecular Graph-Structure Co-Design
https://github.com/zbw001/tapip3d
공식 코드https://github.com/zerchen/gSDF
공식 코드gSDF: Geometry-Driven Signed Distance Functions for 3D Hand-Object Reconstruction
https://github.com/zeromakerplus/netf_public
공식 코드https://github.com/zhaoyiww/fusion4landslide
공식 코드https://github.com/zhenpeiyang/fvor
공식 코드FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction
https://github.com/zju3dv/NeuralRecon
공식 코드NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video
https://github.com/zju3dv/opticfusion
공식 코드https://github.com/zlichen/vectorformer
공식 코드Learning High-resolution Vector Representation from Multi-Camera Images for 3D Object Detection
https://gitlab.com/UnBVision/spawn
공식 코드Data Augmented 3D Semantic Scene Completion with 2D Segmentation Priors
https://github.com/2023-MindSpore-4/Code3/tree/main/DepthNet
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
https://github.com/A4Bio/ADesigner
Conditional Antibody Design as 3D Equivariant Graph Translation
https://github.com/AvrilCheng/LidarStereoNet
https://github.com/DreamTechAI/Direct3D-S2
Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention
https://github.com/EvenJoker/Point-PEFT
Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding
https://github.com/Facebookresearch/deepsdf
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
https://github.com/Fanziapril/mvfnet
https://github.com/Genera1Z/SAVi-PyTorch
https://github.com/GraphPKU/GeoNGNN
https://github.com/JeremyFisher/deep_level_sets
Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers
https://github.com/JeremyFisher/few_shot_3dr
Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers
https://github.com/LIA-DiTella/VesselVAE
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
https://github.com/LaVi-Lab/Video-3D-LLM
Learning from Videos for 3D World: Enhancing MLLMs with 3D Vision Geometry Priors
https://github.com/MasazI/cnn_depth_tensorflow
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
https://github.com/MindCode-4/code-13/tree/main/riemann-noise
Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs
https://github.com/MindSpore-paper-code-3/code8/tree/main/DepthNet
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
https://github.com/MindSpore-scientific-2/code-3/tree/main/ssFPN
https://github.com/MindSpore-scientific/code-14/tree/main/riemann-noise
Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs
https://github.com/MindSpore-scientific/code-5/tree/main/riemann-noise
Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs
https://github.com/Seed3D/Dora
https://github.com/SeokjuLee/Insta-DM
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.